Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs)
The objective of this study is to develop a feed forward neural network (FFNN) model and a radial basis function neural network (RBFNN) model to predict the dissolved oxygen from biochemical oxygen demand (BOD) and chemical oxygen demand (COD) in the Surma River, Bangladesh. The neural network model...
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| Format: | Article |
| Language: | English |
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Elsevier
2017-04-01
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| Series: | Journal of King Saud University: Engineering Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1018363914000385 |
| _version_ | 1811314551033954304 |
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| author | A.A. Masrur Ahmed |
| author_facet | A.A. Masrur Ahmed |
| author_sort | A.A. Masrur Ahmed |
| collection | DOAJ |
| description | The objective of this study is to develop a feed forward neural network (FFNN) model and a radial basis function neural network (RBFNN) model to predict the dissolved oxygen from biochemical oxygen demand (BOD) and chemical oxygen demand (COD) in the Surma River, Bangladesh. The neural network model was developed using experimental data which were collected during a three year long study. The input combinations were prepared based on the correlation coefficient with dissolved oxygen. Performance of the ANN models was evaluated using correlation coefficient (R), mean squared error (MSE) and coefficient of efficiency (E). It was found that the ANN model could be employed successfully in estimating the dissolved oxygen of the Surma River. Comparative indices of the optimized RBFNN with input values of biochemical oxygen demand (BOD) and chemical oxygen demand (COD) for prediction of DO for testing array were MSE = 0.465, E = 0.905 and R = 0.904 and for validation array were MSE = 1.009, E = 0.966 and R = 0.963. Comparing the modeled values by RBFNN and FFNN with the experimental data indicates that neural network model provides reasonable results. |
| first_indexed | 2024-04-13T11:14:41Z |
| format | Article |
| id | doaj.art-aefed929edc940c5b1498a8ec41ade3b |
| institution | Directory Open Access Journal |
| issn | 1018-3639 |
| language | English |
| last_indexed | 2024-04-13T11:14:41Z |
| publishDate | 2017-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Engineering Sciences |
| spelling | doaj.art-aefed929edc940c5b1498a8ec41ade3b2022-12-22T02:49:02ZengElsevierJournal of King Saud University: Engineering Sciences1018-36392017-04-0129215115810.1016/j.jksues.2014.05.001Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs)A.A. Masrur AhmedThe objective of this study is to develop a feed forward neural network (FFNN) model and a radial basis function neural network (RBFNN) model to predict the dissolved oxygen from biochemical oxygen demand (BOD) and chemical oxygen demand (COD) in the Surma River, Bangladesh. The neural network model was developed using experimental data which were collected during a three year long study. The input combinations were prepared based on the correlation coefficient with dissolved oxygen. Performance of the ANN models was evaluated using correlation coefficient (R), mean squared error (MSE) and coefficient of efficiency (E). It was found that the ANN model could be employed successfully in estimating the dissolved oxygen of the Surma River. Comparative indices of the optimized RBFNN with input values of biochemical oxygen demand (BOD) and chemical oxygen demand (COD) for prediction of DO for testing array were MSE = 0.465, E = 0.905 and R = 0.904 and for validation array were MSE = 1.009, E = 0.966 and R = 0.963. Comparing the modeled values by RBFNN and FFNN with the experimental data indicates that neural network model provides reasonable results.http://www.sciencedirect.com/science/article/pii/S1018363914000385Radial basis function neural networkFeed forward neural networkDissolved oxygenSurma River |
| spellingShingle | A.A. Masrur Ahmed Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs) Journal of King Saud University: Engineering Sciences Radial basis function neural network Feed forward neural network Dissolved oxygen Surma River |
| title | Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs) |
| title_full | Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs) |
| title_fullStr | Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs) |
| title_full_unstemmed | Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs) |
| title_short | Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs) |
| title_sort | prediction of dissolved oxygen in surma river by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks anns |
| topic | Radial basis function neural network Feed forward neural network Dissolved oxygen Surma River |
| url | http://www.sciencedirect.com/science/article/pii/S1018363914000385 |
| work_keys_str_mv | AT aamasrurahmed predictionofdissolvedoxygeninsurmariverbybiochemicaloxygendemandandchemicaloxygendemandusingtheartificialneuralnetworksanns |